Precision machine vision inspection systems (or “vision systems” for short) can be utilized to obtain precise dimensional measurements of inspected objects and to inspect various other object characteristics. Such systems may include a computer, a camera and optical system, and a precision workstage that is movable in multiple directions to allow the camera to scan the features of a workpiece that is being inspected. One exemplary prior art system that is commercially available is the QUICK VISION® series of PC-based vision systems and QVPAK® software available from Mitutoyo America Corporation (MAC), located in Aurora, Ill. The features and operation of the QUICK VISION® series of vision systems and the QVPAK® software are generally described, for example, in the QVPAK 3D CNC Vision Measuring Machine User's Guide, published January 2003, and the QVPAK 3D CNC Vision Measuring Machine Operation Guide, published September 1996, each of which is hereby incorporated by reference in their entirety. This series of products, for example, is able to use a microscope-type optical system to provide images of a workpiece at various magnifications, and move the stage as necessary to traverse the workpiece surface beyond the limits of any single video image. A single video image typically encompasses only a portion of the workpiece being observed or inspected, given the desired magnification, measurement resolution, and physical size limitations of such systems.
Machine vision inspection systems generally utilize automated video inspection. U.S. Pat. No. 6,542,180 (the '180 patent) teaches various aspects of such automated video inspection and is incorporated herein by reference in its entirety. As taught in the '180 patent, automated video inspection metrology instruments generally have a programming capability that allows an automatic inspection event sequence to be defined by the user for each particular workpiece configuration. This can be implemented by text-based programming, for example, or through a recording mode which progressively “learns” the inspection event sequence by storing a sequence of machine control instructions corresponding to a sequence of inspection operations performed by a user with the aid of a graphical user interface (GUI), or through a combination of both methods. Such a recording mode is often referred to as “learn mode” or “training mode.” Once the inspection event sequence is defined in “learn mode,” such a sequence can then be used to automatically acquire (and additionally analyze or inspect) images of a workpiece during “run mode.”
The machine control instructions including the specific inspection event sequence (i.e., how to acquire each image and how to analyze/inspect each acquired image) are generally stored as a “part program” or “workpiece program” that is specific to the particular workpiece configuration. For example, a part program defines how to acquire each image, such as how to position the camera relative to the workpiece, at what lighting level, at what magnification level, etc. Further, the part program defines how to analyze/inspect an acquired image, for example, by using one or more video tools such as edge/boundary detection video tools.
Video tools (or “tools” for short) and other GUI features may be set up manually to accomplish inspection and/or other machine control operations. Video tools' set-up parameters and operations can also be recorded during learn mode, in order to create automatic inspection programs, or “part programs,” which incorporate measurement/analytical operations to be performed by various video tools. Video tools may include, for example, edge/boundary detection tools, autofocus tools, shape or pattern matching tools, dimension measuring tools, and the like. Other GUI features may include dialog boxes related to data analysis, step and repeat loop programming—as disclosed, for example, in U.S. Pat. No. 8,271,895, which is incorporated herein by reference in its entirety—etc. For example, such tools and GUI features are routinely used in a variety of commercially available machine vision inspection systems, such as the QUICK VISION® series of vision systems and the associated QVPAK® software, discussed above.
Machine vision inspection systems are known to incorporate various types of focus measurement in automated video inspection, for controlling continuous auto focus and/or for making surface height measurements. Generally there are two types of focus measurement: image-based focus measurement and signal-based focus measurement. The image-based focus measurement is based on analysis of the contrast in acquired images. For a given field of view, the highest contrast image generally corresponds to the best focused image. A surface height measurement may be inferred from the best focused image position, since the camera-object distance corresponding to any image is generally known in machine vision inspection systems.
The signal-based focus measurement is based on the use of an auxiliary focus sensor that does not rely on the images of the machine vision inspection system for determining the best focus position or surface height. Various types of auxiliary focus sensors are known, including triangulation sensors, knife edge focus sensors, chromatic confocal sensors, Shack-Hartmann type wavefront sensors, etc., as described in U.S. Pat. Nos. 4,336,997, 4,950,878, 6,184,974, 7,301,133, 7,723,657 and 7,728,961, which are incorporated by reference herein. Generally, auxiliary focus sensors perform focus measurement by receiving optical signals from an object, converting them to electrical signals, and comparing them with a reference signal corresponding to the best focus (zero) position or a workpiece (object) surface height. Thus, by determining that an obtained electrical signal is above or below the reference signal by how much, it can be determined that an image is out of focus in a positive (+) or a negative (−) direction by how much along the optical (e.g., vertical) axis relative to the best focus (zero) position of the object lens. Typically, the signal-based focus measurement is faster than the image-based focus measurement and thus is suited for application in a Tracking AutoFocus (TAF) sensor that is configured to continuously and automatically maintain focus in a vision system in real time.
The present invention is directed to providing an improved system, GUI and computer-implemented method for controlling a TAF sensor in a machine vision inspection system.